

from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
	import timm


if is_torch_available():
	from torch import Tensor



class        A__(a_,  a_							):
						"""simple docstring"""






						_A    :				Dict       					=			'''pixel_values'''
						_A    :				Dict       					=			False
						_A    :				Dict       					=			TimmBackboneConfig





						def __init__(    self  ,      _lowercase  ,      **_lowercase    )     -> List[Any]:
									requires_backends(self  ,      """timm"""    )
									super().__init__(_lowercase    )
									a_						:						str 							= config

									if config.backbone is None:
												raise ValueError("""backbone is not set in the config. Please set it to a timm model name."""    )

									if config.backbone not in timm.list_models():
												raise ValueError(F'''backbone {config.backbone} is not supported by timm.'''    )

									if hasattr(_lowercase  ,      """out_features"""    ) and config.out_features is not None:
												raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""    )

									a_						:						List[str] 							= getattr(_lowercase  ,      """use_pretrained_backbone"""  ,      _lowercase    )
									if pretrained is None:
												raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False."""    )

									# We just take the final layer by default. This matches the default for the transformers models.
									a_						:						Tuple 							= config.out_indices if getattr(_lowercase  ,      """out_indices"""  ,      _lowercase    ) is not None else (-1,)

									a_						:						Union[str, Any] 							= timm.create_model(
									    config.backbone  ,      pretrained=_lowercase  ,      features_only=config.features_only  ,      in_chans=config.num_channels  ,      out_indices=_lowercase  ,      **_lowercase  ,      )
									# These are used to control the output of the model when called. If output_hidden_states is True, then
									# return_layers is modified to include all layers.
									a_						:						Tuple 							= self._backbone.return_layers
									a_						:						int 							= {layer["""module"""]: str(_lowercase    ) for i, layer in enumerate(self._backbone.feature_info.info    )}
									super()._init_backbone(_lowercase    )





						@classmethod
						def 			UpperCamelCase__					(    cls  ,      _lowercase  ,      *_lowercase  ,      **_lowercase    )     -> Optional[int]:
									requires_backends(cls  ,      ["""vision""", """timm"""]    )
									from ...models.timm_backbone import TimmBackboneConfig

									a_						:						List[str] 							= kwargs.pop("""config"""  ,      TimmBackboneConfig()    )

									a_						:						List[Any] 							= kwargs.pop("""use_timm_backbone"""  ,      _lowercase    )
									if not use_timm:
												raise ValueError("""use_timm_backbone must be True for timm backbones"""    )

									a_						:						Optional[int] 							= kwargs.pop("""num_channels"""  ,      config.num_channels    )
									a_						:						List[Any] 							= kwargs.pop("""features_only"""  ,      config.features_only    )
									a_						:						int 							= kwargs.pop("""use_pretrained_backbone"""  ,      config.use_pretrained_backbone    )
									a_						:						Optional[Any] 							= kwargs.pop("""out_indices"""  ,      config.out_indices    )
									a_						:						Optional[Any] 							= TimmBackboneConfig(
									    backbone=_lowercase  ,      num_channels=_lowercase  ,      features_only=_lowercase  ,      use_pretrained_backbone=_lowercase  ,      out_indices=_lowercase  ,      )
									return super()._from_config(_lowercase  ,      **_lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> Union[str, Any]:
									pass





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      **_lowercase    )     -> Union[BackboneOutput, Tuple[Tensor, ...]]:
									a_						:						Union[str, Any] 							= return_dict if return_dict is not None else self.config.use_return_dict
									a_						:						Any 							= (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									a_						:						Any 							= output_attentions if output_attentions is not None else self.config.output_attentions

									if output_attentions:
												raise ValueError("""Cannot output attentions for timm backbones at the moment"""    )

									if output_hidden_states:
												# We modify the return layers to include all the stages of the backbone
												a_						:						List[str] 							= self._all_layers
												a_						:						str 							= self._backbone(_lowercase  ,      **_lowercase    )
												a_						:						Dict 							= self._return_layers
												a_						:						Tuple 							= tuple(hidden_states[i] for i in self.out_indices    )
									else:
												a_						:						Optional[Any] 							= self._backbone(_lowercase  ,      **_lowercase    )
												a_						:						int 							= None

									a_						:						int 							= tuple(_lowercase    )
									a_						:						List[Any] 							= tuple(_lowercase    ) if hidden_states is not None else None

									if not return_dict:
												a_						:						str 							= (feature_maps,)
												if output_hidden_states:
															a_						:						Union[str, Any] 							= output + (hidden_states,)
												return output

									return BackboneOutput(feature_maps=_lowercase  ,      hidden_states=_lowercase  ,      attentions=_lowercase    )





__snake_case				:      int   		=			[
    """Audio""",
    """Array2D""",
    """Array3D""",
    """Array4D""",
    """Array5D""",
    """ClassLabel""",
    """Features""",
    """Sequence""",
    """Value""",
    """Image""",
    """Translation""",
    """TranslationVariableLanguages""",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages


